Description:

The Personalization (PZN) team at Spotify aims to enhance the listening experience by providing tailored recommendations for music, podcasts, and audiobooks.
The team is responsible for products like Home, Search, and playlists such as Made For You, Discover Weekly, and Daily Mix, which are used by hundreds of millions of people globally.
The role offers flexibility to work within the North America region, with collaboration occurring in the Eastern Standard time zone.
Responsibilities include being a technical leader, coordinating technical projects across teams, facilitating collaboration with engineers, product owners, and designers, and being a member of an autonomous, cross-functional agile team.
The position involves architecting, designing, developing, and deploying machine learning models, as well as contributing to Spotify’s ML community.

Requirements:

Candidates should have experience as a technical leader or mentor, particularly in training and operating internet-scale machine learning models.
A strong background in machine learning, especially with recommender systems, is required.
Experience in designing end-to-end tech specifications and modular architectures for ML frameworks in collaboration with product teams is necessary.
Candidates must have experience in designing and building machine learning systems, as well as feature engineering and building scalable data pipelines.
A deep understanding of machine learning systems and infrastructure is essential.
Proficiency in TensorFlow or PyTorch is required, with experience in Kubeflow or Ray considered a plus.
Familiarity with generative models is also a plus.

The United States base salary range for this position is between $176,166 and $251,666, plus equity.
Benefits include health insurance, six months of paid parental leave, a 401(k) retirement plan, a monthly meal allowance, 23 paid days off, 13 paid flexible holidays, and paid sick leave.
Spotify is committed to inclusivity and provides reasonable accommodations during the recruitment process to support all candidates.